Introduction

Our data is derived from Instagram accounts and comes from the website known as Kaggle.com.

Our data contains usernames, followings, followers, likes, comments, and locations of different accounts.

We added some columns in our data such as engagement, engagement_quantile, post_timestamp, and caption_length.

This data is interesting because it has a large sample of different accounts where we can draw conclusions about patterns in engagement scores. We also compare and contrast some things from our data.

A Link to the data https://www.kaggle.com/datasets/propriyam/instagram-data

Problem Statement and Questions

We choose this data to understand Instagram engagement trends and the factors which contributes to post and videos.

What trends do we see based on posts with the highest engagement?
When do posts get the most engagement?
What is the relationship between caption length and engagement?
From pictures, carousel, and videos which one get the most highest engagement?
From pictures and videos which one get the most comments and likes?

What Libraries did we use?

library(tidyverse) Use to filter the data.
library(lubridate) Use to format dates.
library(stringr) Use to provides a set of functions designed to make work with strings.
library(dplyr) Use to provide a function for each basic verb of data manipulation.
library(plotly) Use to make interactive graphs.

Lets take a look at Unfiltered Data

insta_data <- read_csv("instagram_data.csv")
glimpse(insta_data)

## Rows: 11,692
## Columns: 14
## $ owner_id        <chr> "36063641", "36063641", "36063641", "36063641", "36063…
## $ owner_username  <chr> "christendominique", "christendominique", "christendom…
## $ shortcode       <chr> "C3_GS1ASeWI", "C38ivgNS3IX", "C35-Dd9SO1b", "C33TadDM…
## $ is_video        <lgl> FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…
## $ caption         <chr> "I’m a brunch & Iced Coffee girlie☕️🍳 \n\nTop @ta3 X …
## $ comments        <dbl> 268, 138, 1089, 271, 145, 143, 356, 132, 128, 884, 211…
## $ likes           <dbl> 16382, 9267, 10100, 6943, 17158, 9683, 42906, 4287, 74…
## $ created_at      <dbl> 1709326758, 1709241048, 1709154707, 1709065322, 170871…
## $ location        <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ imageUrl        <chr> "https://instagram.flba2-1.fna.fbcdn.net/v/t39.30808-6…
## $ multiple_images <lgl> TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
## $ username        <chr> "christendominique", "christendominique", "christendom…
## $ followers       <dbl> 2144626, 2144626, 2144626, 2144626, 2144626, 2144626, …
## $ following       <dbl> 1021, 1021, 1021, 1021, 1021, 1021, 1021, 1021, 1021, …

Mutated Columns

Key terms

Engagement refers to the actual score from the data.
engagement_quantile refers to the follower count divided into four different quarters.
post_timestamp refers to the time when pictures or videos was posted.
caption_length refers to the length of the caption.

(Added new columns which represent 1 as the lowest followers, 2 and 3 as the average followers and the 4 as the highest followers).

new_data<- insta_data %>% mutate(engagement = round((((likes+comments)/followers)*100),digits = 2),
                                 follower_quantile = ntile(followers,4),
                                 engagement_quantile = ntile(engagement,4),
                                 post_timestamp = as_datetime(created_at),
                                 post_time = format(round(post_timestamp,units = "hours"),format = "%H:%M"),
                                 caption_length = lengths(strsplit(caption, ' ')))

Filtered Data

Our original data was messed up so we added new columns with calculated values.

glimpse(new_data)

## Rows: 11,692
## Columns: 20
## $ owner_id            <chr> "36063641", "36063641", "36063641", "36063641", "3…
## $ owner_username      <chr> "christendominique", "christendominique", "christe…
## $ shortcode           <chr> "C3_GS1ASeWI", "C38ivgNS3IX", "C35-Dd9SO1b", "C33T…
## $ is_video            <lgl> FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, T…
## $ caption             <chr> "I’m a brunch & Iced Coffee girlie☕️🍳 \n\nTop @ta…
## $ comments            <dbl> 268, 138, 1089, 271, 145, 143, 356, 132, 128, 884,…
## $ likes               <dbl> 16382, 9267, 10100, 6943, 17158, 9683, 42906, 4287…
## $ created_at          <dbl> 1709326758, 1709241048, 1709154707, 1709065322, 17…
## $ location            <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ imageUrl            <chr> "https://instagram.flba2-1.fna.fbcdn.net/v/t39.308…
## $ multiple_images     <lgl> TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FA…
## $ username            <chr> "christendominique", "christendominique", "christe…
## $ followers           <dbl> 2144626, 2144626, 2144626, 2144626, 2144626, 21446…
## $ following           <dbl> 1021, 1021, 1021, 1021, 1021, 1021, 1021, 1021, 10…
## $ engagement          <dbl> 0.78, 0.44, 0.52, 0.34, 0.81, 0.46, 2.02, 0.21, 0.…
## $ follower_quantile   <int> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1,…
## $ engagement_quantile <int> 3, 2, 3, 2, 3, 2, 4, 2, 2, 4, 2, 2, 1, 3, 4, 2, 3,…
## $ post_timestamp      <dttm> 2024-03-01 20:59:18, 2024-02-29 21:10:48, 2024-02…
## $ post_time           <chr> "21:00", "21:00", "21:00", "20:00", "20:00", "20:0…
## $ caption_length      <int> 12, 34, 81, 57, 17, 66, 50, 17, 8, 53, 17, 20, 90,…

Key terms

is_video refers to the videos posted on Instagram account.
caption refers to the titles on the Instagram posts.
comments/likes refers to the followers response to the posts.
created_at refers to the coded time stamp of when the post was created.
multiple_images refers to the boolean of whether the post was a carousel or multiple image upload.
followers/following refers to the users.

Reference of Account Followers Distribution

Insights on the account follower distribution mean 1 is the lowest, 4 is the highest.

new_data %>% group_by(follower_quantile) %>% summarise(follower_mean = format(round(mean(followers),0),big.mark=','))

## # A tibble: 5 × 2
##   follower_quantile follower_mean
##               <int> <chr>        
## 1                 1 108,262      
## 2                 2 342,149      
## 3                 3 834,535      
## 4                 4 8,559,178    
## 5                NA NA

What trends do we see based on posts with the highest engagement?

Low amount of followers have the highest engagement whereas high amount of followers have the lowest engagement.

new_data %>% filter(engagement != is.na(engagement)) %>% 
  group_by(follower_quantile) %>% summarise(avg_eng = mean(engagement)) %>% 
  ggplot(aes(x = follower_quantile,y = avg_eng)) +
  geom_line() + labs(x='Follower Count Quartile',y = 'Average Engagement')

When do posts get the most engagement?

This is showing average engagement percent by post local time.

new_data %>% filter(engagement != is.na(engagement)) %>% group_by(post_time) %>% summarise(mean(engagement),n())

## # A tibble: 24 × 3
##    post_time `mean(engagement)` `n()`
##    <chr>                  <dbl> <int>
##  1 00:00                   2.08   261
##  2 01:00                   1.88   267
##  3 02:00                   1.71   238
##  4 03:00                   2.99   178
##  5 04:00                   2.37   138
##  6 05:00                   3.34   125
##  7 06:00                   2.38   145
##  8 07:00                   1.59   185
##  9 08:00                   3.81   223
## 10 09:00                   1.93   293
## # ℹ 14 more rows

Reference of the Post Engagement

We see the most engagement between the hours of 5am, 8am, 12pm, 1pm, 4pm and 5pm, during peak times of the day.

time_eng <- new_data %>% filter(engagement != is.na(engagement)) %>% group_by(post_time) %>% summarise(eng_mean = round(mean(engagement),1)) %>% 
  ggplot(aes(x = eng_mean,y = post_time,fill = as.factor(post_time))) +
  geom_col(stat = 'identity') +
  scale_x_continuous(labels = waiver()) +
  labs(y = 'Posting Time', x = "Avg Engagement %") +
  scale_fill_manual(values = c('05:00'="tomato",'08:00'="tomato",'12:00'="tomato",'13:00'="tomato",'16:00'="tomato",'17:00'="tomato"), guide = FALSE)

ggplotly(time_eng)

What is the relationship between caption length and engagement?

Highest engagement posts include captions with lengths x & y. The graph shows that the long caption gains more engagement.

new_data %>% filter(engagement != is.na(engagement)) %>% 
  mutate(caption_bucket = case_when(caption_length < 50 ~"<50",caption_length >=50 & caption_length<100~"50-100",caption_length>=100 & caption_length < 150 ~ "100-150",caption_length>=150 & caption_length < 200 ~ "150-200",caption_length>=200 & caption_length < 250 ~ "200-250",caption_length>=250 & caption_length < 300 ~ "250-300",caption_length>=300 & caption_length < 350 ~ "300-350",caption_length > 350 ~"350+")) %>% group_by(caption_bucket) %>%
  summarise(avg_eng = mean(engagement)) %>% ggplot(aes(x = caption_bucket,y = avg_eng))+geom_point(size = 5) + labs(x = "Caption Length",y = "Average Engagement")+ scale_x_discrete(limits = c("<50","50-100","100-150","150-200","200-250","250-300","300-350","350+"))

From pictures, carousel and videos which one get the most highest engagement?

The graph clearly show that the single images get more engagement whereas, carousels get less.

new_data %>% filter(engagement != is.na(engagement)) %>% mutate(type = case_when(is_video == TRUE & multiple_images == FALSE ~"Video",
                                    is_video == FALSE & multiple_images == FALSE ~ "Picture",
                                    multiple_images == TRUE ~ "Carousel")) %>% 
  group_by(type) %>% summarise(avg_eng = mean(engagement)) %>% 
  ggplot(aes(x = type,y = avg_eng))+
  geom_col() + labs(x = 'Content Type',y = 'Average Engagement')

From pictures and videos which one get the most comment and likes?

We see pictures get more comments and likes than videos.

new_data %>% 
  ggplot(aes(x = likes, y = comments, group = is_video, color = is_video)) + geom_point() +scale_y_log10() + scale_x_log10() + scale_color_manual(name = "Type", labels = c("Picture", "Video"),values = c("blue","red"))

Summary

In summary, our presentation emphasized that our Instragram accounts data have following:

Highest engagement posts have lowest followers.
Highest engagement posts happen between afternoon hours.
Highest engagement posts have long captions.
Single images get more engagement than carousels.
Pictures get more likes and comments than videos.

Instagram Data

Introduction

Problem Statement and Questions

What Libraries did we use?

Lets take a look at Unfiltered Data

Mutated Columns

Key terms

Filtered Data

Key terms

Reference of Account Followers Distribution

What trends do we see based on posts with the highest engagement?

When do posts get the most engagement?

Reference of the Post Engagement

What is the relationship between caption length and engagement?

From pictures, carousel and videos which one get the most highest engagement?

From pictures and videos which one get the most comment and likes?

Summary